CLO: The cell line ontology

Sarntivijai, Sirarat; Lin, Yuling; Xiang, Zuoshuang; Meehan, Terrence F.; Diehl, Alexander D.; Vempati, Uma D.; Schürer, Stephan C.; Pang, Chao; Malone, James; Parkinson, Helen; Liu, Yue; Takatsuki, Terue; Saijo, Kaoru; Masuya, Hiroshi; Nakamura, Yukio; Brush, Matthew; Haendel, Melissa; Zheng, Jie; Stoeckert, Christian J.; Peters, Bjoern; Mungall, Christopher J.; Carey, Thomas E.; States, David J.; Athey, Brian D.; He, Yongqun

doi:10.1186/2041-1480-5-37

Cited by 106 publications

(92 citation statements)

References 18 publications

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“…In order to answer whether this part is a plausible addition to the ODP, we would need to run another study. Another finding that validates the mined patterns comes from Sarntivijai et al [30]. The paper describes the addition of 1,622 new cell lines from the Japan RIKEN Cell Bank to CLO, which is evidenced in our discovered frequent axiom pattern: SubClassOf ‘is in cell line repository’ value ‘RIKEN Cell Bank’, with an absolute support of 1,622.…”

Section: Experiments and Resultssupporting

confidence: 80%

“…Figure 15 shows the CFPs that we have automatically mined for the Cell Line Ontology (CLO). After a manual investigation, we have subsequently established that the mined patterns reflect the ‘Cell Line Cells’ design pattern proposed by Sarntivijai et al [30]. CLO is one of the largest BioPortal ontologies from our dataset (it contains 114,843 SubClassOf and EquivalentTo axioms).…”

Section: Experiments and Resultsmentioning

confidence: 75%

“…a) ’Cell Line Cells’ design pattern for the CLO ontology [30] (top). b) The selected corresponding class–frame fragments, which we have automatically mined (middle and bottom part of the figure).…”

Section: Figmentioning

confidence: 99%

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Discovery of emerging design patterns in ontologies using tree mining

Ławrynowicz

Potoniec

Robaczyk

et al. 2018

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The research goal of this work is to investigate modeling patterns that recur in ontologies. Such patterns may originate from certain design solutions, and they may possibly indicate emerging ontology design patterns. We describe our tree-mining method for identifying the emerging design patterns. The method works in two steps: (1) we transform the ontology axioms in a tree shape in order to find axiom patterns; and then, (2) we use association analysis to mine co-occuring axiom patterns in order to extract emerging design patterns. We conduct an experimental study on a set of 331 ontologies from the BioPortal repository. We show that recurring axiom patterns appear across all individual ontologies, as well as across the whole set. In individual ontologies, we find frequent and non-trivial patterns with and without variables. Some of the former patterns have more than 300,000 occurrences. The longest pattern without a variable discovered from the whole ontology set has size 12, and it appears in 14 ontologies. To the best of our knowledge, this is the first method for automatic discovery of emerging design patterns in ontologies. Finally, we demonstrate that we are able to automatically detect patterns, for which we have manually confirmed that they are fragments of ontology design patterns described in the literature. Since our method is not specific to particular ontologies, we conclude that we should be able to discover new, emerging design patterns for arbitrary ontology sets.

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Section: Experiments and Resultssupporting

confidence: 80%

Section: Experiments and Resultsmentioning

confidence: 75%

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Discovery of emerging design patterns in ontologies using tree mining

Ławrynowicz

Potoniec

Robaczyk

et al. 2018

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“…Controlled vocabularies and ontologies pioneered by SNOMED 126 providing standardised anatomical descriptors related to tissue types (BRENDA) 127,128 , cell types (Cell Ontology) 129 , and human diseases (DOID, http://disease-ontology.org/) 130 tissue. An assessment of the number of reported olfactory receptors in Kim et al 73 and Wilhelm et al 132 25 by Ezkurdia et al 133 showed that these two large-scale studies had used poor MS/MS spectra to identify more than 100 olfactory receptors each, despite the fact that they did not include data from nasal tissue.…”

Section: Human Sample Resourcesmentioning

confidence: 99%

Quest for Missing Proteins: Update 2015 on Chromosome-Centric Human Proteome Project

Horvatovich¹,

Lundberg²,

Chen³

et al. 2015

J. Proteome Res.

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This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-HPP) consortium, which develops new technologies to identify yet-to-be annotated proteins (termed "missing proteins") in biological samples that lack sufficient experimental evidence at the protein level for confident protein identification. The C-HPP also aims to identify new protein forms that may be caused by genetic variability, post-translational modifications, and alternative splicing. Proteogenomic data integration forms the basis of the C-HPP's activities; therefore, we have summarized some of the key approaches and their roles in the project. We present new analytical technologies that improve the chemical space and lower detection limits coupled to bioinformatics tools and some publicly available resources that can be used to improve data analysis or support the development of analytical assays. Most of this paper's content has been compiled from posters, slides, and discussions presented in the series of C-HPP workshops held during 2014. All data (posters, presentations) used are available at the C-HPP Wiki (http://c-hpp.webhosting.rug.nl/) and in the Supporting Information.

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“…Expression analyses and RNASeq experiments often draw their samples from particular cell types and projects such as ENCODE [ 16 ] and FANTOM [ 17 ] are using the cell type ontology to indicate the source cell type for their data. In addition, there are coordinated efforts connecting the cell line ontology, used in cancer studies, to the cell type ontology to indicate the original cell type [ 18 ]. There is immense benefi t to constructing any ontology from its most element components because it provides a connective route across the widest possible network of projects.…”

Section: Orthogonalitymentioning

confidence: 99%

The Vision and Challenges of the Gene Ontology

Lewis

2016

Methods in Molecular Biology

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The overarching goal of the Gene Ontology (GO) Consortium is to provide researchers in biology and biomedicine with all current functional information concerning genes and the cellular context under which these occur. When the GO was started in the 1990s surprisingly little attention had been given to how functional information about genes was to be uniformly captured, structured in a computable form, and made accessible to biologists. Because knowledge of gene, protein, ncRNA, and molecular complex roles is continuously accumulating and changing, the GO needed to be a dynamic resource, accurately tracking ongoing research results over time. Here I describe the progress that has been made over the years towards this goal, and the work that still remains to be done, to make of the Gene Ontology (GO) Consortium realize its goal of offering the most comprehensive and up-to-date resource for information on gene function. Key words Gene Ontology , Gene function , Genomics , Biological modeling MotivationFrom their outset in the early 1990s it was obvious that biological databases demanded a methodical way of describing the function of genes. For one thing, a model system's raison d ' etre was to gain insight into human health and, in the days before entire genomes and proteomes were available, the relevant connections to human biology were largely based on textual descriptions of biological role. In conjunction, as genomes such as yeast were being completed, new laboratory techniques were being developed for surveying the genome, such as microarray expression panels, and these data cried out for systematic description of the voluminous results. Finally, lest we forget, this period also saw the advent of the "World Wide Web." The early pioneers in biological databases were quick to take advantage of the latest technologies for data dissemination (much easier than shipping a copy of GenBank on tape or disk drive as was the norm), but exchanging data in a rational and efficient manner required concomitant syntactic and semantic

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CLO: The cell line ontology

Cited by 106 publications

References 18 publications

Discovery of emerging design patterns in ontologies using tree mining

Discovery of emerging design patterns in ontologies using tree mining

Quest for Missing Proteins: Update 2015 on Chromosome-Centric Human Proteome Project

The Vision and Challenges of the Gene Ontology

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